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題名 探究混合二維卜瓦松模型與混合二維常態模型之關聯
作者 郝飛洋
Hao, Fei-Yang
貢獻者 鄭宗記
Cheng, Tsung-Chi
郝飛洋
Hao, Fei-Yang
關鍵詞 卜瓦松分配
常態分配
卜瓦松分配趨近於常態分配
雙變量
混合
日期 2021
上傳時間 2-九月-2021 15:37:18 (UTC+8)
摘要 在估計股票市場事前交易機率(Probability of Informed trading)時， Duarte 和 Young（2009）使用了三個雙變量卜瓦松混合模型，由於參數數量，股票買賣量巨大等問題，導致參數估計的結果不如預期。我們想到在樣本資料符合混合雙變數卜瓦松分配且參數很大時，若使用混合雙變數常態分配模型進行參數估計得到的估計結果，相較於使用混合雙變量卜瓦松分配來估計時，是否也能得到表現良好的結果？本篇論文通過模擬的方式對此問題進行進一步的探討。經過對模擬結果進行分析，我們發現隨著在卜瓦松分配參數增大，常態分配模型與卜瓦松分配模型的參數估計結果越來越接近。
參考文獻 Acharya, Viral V. and Johnson, Timothy C., Insider trading in credit derivatives. J. Financ. Econ., 2007, 84(1), 110–141.
Alessandro, B., & Pier, A., F, Simulation of correlated Poisson variables. Applied Stochastic Models in Business and Industry, 2015, 31, 669-680.
Ali, Ashiq, Klasa, Sandy and Zhen Li, Oliver, Institutional stakeholdings and better informed traders at earnings announcements. J. Account. Econ., 2008, 46(1), 47–61.
Ascioglu, Asli, Hegde, Shantaram P. and McDermott, John B., Information asymmetry and investment-cash flow sensitivity. J. Bank. Finance, 2008, 32(6), 1036–1048.
Bharath, Sreedhar T., Pasquariello, Paolo and Wu, Guojun, Does asymmetric information drive capital structure decisions?. Rev. Financ. Studies, 2009, 22(8), 3211–3243.
Brown, Stephen, Hillegeist, Stephen A. and Lo, Kin, Conference calls and information asymmetry. J. Account. Econ., 2004, 37(3), 343–366.
Brown, Stephen, Hillegeist, Stephen A. and Lo, Kin, The effect of earnings surprises on information asymmetry. J. Account. Econ., 2009, 47(3), 208–225.
Chen, Yifan and Zhao, Huainan, Informed trading, information uncertainty, and price momentum. J. Bank. Finance, 2012, 36(7), 2095–2109.
Duarte, Jefferson, Hu, Edwin and Young, Lance, A comparison of some structural models of private information arrival. J. Financ. Econ., 2020, 135(3), 795–815.
Duarte, Jefferson and Young, Lance, Why is PIN priced?. J. Financ. Econ., 2009, 91, 119–138.
Easley, David, Hvidkjaer, Soeren and O’Hara, Maureen, Is information risk a determinant of asset returns?. J. Finance, 2002, 57(5), 2185–2221.
Easley, David, Hvidkjaer, Soeren and O’Hara, Maureen, Factoring information into returns. J. Financ. Q. Anal., 2010, 45(2), 293–309.
Easley, David, Kiefer, Nicholas M. and O’Hara, Maureen, The information content of the trading process. J. Empir. Finance, 1997, 4, 159–186.
Easley, David, Kiefer, Nicholas M., O’Hara, Maureen and Paperman, Joseph B., Liquidity, information, and infrequently traded stocks. J. Finance, 1996, 51(4), 1405–1435.
Ellul, Andrew and Pagano, Marco, IPO underpricing and after-Market liquidity. Rev. Financ. Studies, 2006, 19(2), 381–421.
Ersan, Oguz and Alıcı, Aslı, An unbiased computation methodology for estimating the probability of informed trading (PIN). J. Int. Financ. Markets, Inst. Money, 2016, 43, 74–94.
Finch, Stephen J., Mendell, Nancy R. and Thode,Jr, Henry C., Probabilistic measures of adequacy of a numerical searchfor a global maximum. J. Am. Stat. Assoc., 1989, 84(408), 1020–1023.
Holgate, P, Estimation for the Bivariate Poisson Distribution. Biomettrika, 1964, 51(1/2), 241-245.
Inbal, Y., & Galit, S, On generating multivariate Poisson data in management science applications. Applied Stochastic Models in Business and Ind¬ustry, 2012, 28, 91-102.
Kang, Qiang and Liu, Qiao, Stock trading, information production, and executive incentives. J. Corporate Finance, 2008, 14(4), 484–498.
Karlis, Dimitris and Xekalaki, Evdokia, Choosing initial values for the EM algorithm for finite mixtures. Computat. Stat. Data Anal., 2003, 41(3-4), 577–590.
Lee, Charles M.C. and Ready, Mark J., Inferring trade direction from intraday data. J. Finance, 1991, 46(2), 733–746.
Li, Haitao, Wang, Junbo, Wu, Chunchi and He, Yan, Are liquidity and information risks priced in the treasury bond market?. J. Finance, 2009, 64(1), 467–503.
Lin, Hsiou-Wei William and Ke, Wen-Chyan, A computing bias in estimating the probability of informed trading. J. Financ. Markets, 2011, 14(4), 625–640.
Madsen, L, & Dalthorp, D, Simulating correlated count data. Environmental and Ecological Statistics, 2007, 14, 129-148.
Tom, B., & Dimitris, K, A multivariate Poisson mixture model for marketing applications. Statistica Neerlandica, 2004, 58(3), 322-348.
描述 碩士
國立政治大學
統計學系
106354030
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0106354030
資料類型 thesis
dc.contributor.advisor 鄭宗記zh_TW
dc.contributor.advisor Cheng, Tsung-Chien_US
dc.contributor.author (作者) 郝飛洋zh_TW
dc.contributor.author (作者) Hao, Fei-Yangen_US
dc.creator (作者) 郝飛洋zh_TW
dc.creator (作者) Hao, Fei-Yangen_US
dc.date (日期) 2021en_US
dc.date.accessioned 2-九月-2021 15:37:18 (UTC+8)-
dc.date.available 2-九月-2021 15:37:18 (UTC+8)-
dc.date.issued (上傳時間) 2-九月-2021 15:37:18 (UTC+8)-
dc.identifier (其他 識別碼) G0106354030en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/136828-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 統計學系zh_TW
dc.description (描述) 106354030zh_TW
dc.description.abstract (摘要) 在估計股票市場事前交易機率(Probability of Informed trading)時， Duarte 和 Young（2009）使用了三個雙變量卜瓦松混合模型，由於參數數量，股票買賣量巨大等問題，導致參數估計的結果不如預期。我們想到在樣本資料符合混合雙變數卜瓦松分配且參數很大時，若使用混合雙變數常態分配模型進行參數估計得到的估計結果，相較於使用混合雙變量卜瓦松分配來估計時，是否也能得到表現良好的結果？本篇論文通過模擬的方式對此問題進行進一步的探討。經過對模擬結果進行分析，我們發現隨著在卜瓦松分配參數增大，常態分配模型與卜瓦松分配模型的參數估計結果越來越接近。zh_TW
dc.description.tableofcontents 第一章 緒論 1
第一節 研究動機與目的 1
第二節 研究架構 2
第二章 研究方法 3
第一節 卜瓦松分配 3
1 單維度卜瓦松分配 3
2 二維卜瓦松分配 4
第二節 混合常態分配 5
1 混合單維度常態分配 5
2 混合多維度常態分配 5
3 EM演算法估計k群m維混合常態分配模型 5
第三節 混合卜瓦松分配 7
1 混合單維度卜瓦松分配 7
2 混合二維度卜瓦松分配 7
3 EM演算法估計k群混合二維度卜瓦松分配模型 8
第三章 模擬分析 10
第一節 混合單維度卜瓦松分配漸進混合常態分配 10
1.1 模擬目的 10
1.2 模擬設計 10
1.3 模擬結果分析 11
第二節 二維卜瓦松分配漸進二維常態分配 31
2.1 模擬目的 31
2.2 模擬設計 31
2.3 模擬結果分析 32
第四章 結論 47
參考文獻 48		zh_TW
dc.format.extent 3346748 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106354030en_US
dc.subject (關鍵詞) 卜瓦松分配zh_TW
dc.subject (關鍵詞) 常態分配zh_TW
dc.subject (關鍵詞) 卜瓦松分配趨近於常態分配zh_TW
dc.subject (關鍵詞) 雙變量zh_TW
dc.subject (關鍵詞) 混合zh_TW
dc.title (題名) 探究混合二維卜瓦松模型與混合二維常態模型之關聯zh_TW
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Acharya, Viral V. and Johnson, Timothy C., Insider trading in credit derivatives. J. Financ. Econ., 2007, 84(1), 110–141.
Alessandro, B., & Pier, A., F, Simulation of correlated Poisson variables. Applied Stochastic Models in Business and Industry, 2015, 31, 669-680.
Ali, Ashiq, Klasa, Sandy and Zhen Li, Oliver, Institutional stakeholdings and better informed traders at earnings announcements. J. Account. Econ., 2008, 46(1), 47–61.
Ascioglu, Asli, Hegde, Shantaram P. and McDermott, John B., Information asymmetry and investment-cash flow sensitivity. J. Bank. Finance, 2008, 32(6), 1036–1048.
Bharath, Sreedhar T., Pasquariello, Paolo and Wu, Guojun, Does asymmetric information drive capital structure decisions?. Rev. Financ. Studies, 2009, 22(8), 3211–3243.
Brown, Stephen, Hillegeist, Stephen A. and Lo, Kin, Conference calls and information asymmetry. J. Account. Econ., 2004, 37(3), 343–366.
Brown, Stephen, Hillegeist, Stephen A. and Lo, Kin, The effect of earnings surprises on information asymmetry. J. Account. Econ., 2009, 47(3), 208–225.
Chen, Yifan and Zhao, Huainan, Informed trading, information uncertainty, and price momentum. J. Bank. Finance, 2012, 36(7), 2095–2109.
Duarte, Jefferson, Hu, Edwin and Young, Lance, A comparison of some structural models of private information arrival. J. Financ. Econ., 2020, 135(3), 795–815.
Duarte, Jefferson and Young, Lance, Why is PIN priced?. J. Financ. Econ., 2009, 91, 119–138.
Easley, David, Hvidkjaer, Soeren and O’Hara, Maureen, Is information risk a determinant of asset returns?. J. Finance, 2002, 57(5), 2185–2221.
Easley, David, Hvidkjaer, Soeren and O’Hara, Maureen, Factoring information into returns. J. Financ. Q. Anal., 2010, 45(2), 293–309.
Easley, David, Kiefer, Nicholas M. and O’Hara, Maureen, The information content of the trading process. J. Empir. Finance, 1997, 4, 159–186.
Easley, David, Kiefer, Nicholas M., O’Hara, Maureen and Paperman, Joseph B., Liquidity, information, and infrequently traded stocks. J. Finance, 1996, 51(4), 1405–1435.
Ellul, Andrew and Pagano, Marco, IPO underpricing and after-Market liquidity. Rev. Financ. Studies, 2006, 19(2), 381–421.
Ersan, Oguz and Alıcı, Aslı, An unbiased computation methodology for estimating the probability of informed trading (PIN). J. Int. Financ. Markets, Inst. Money, 2016, 43, 74–94.
Finch, Stephen J., Mendell, Nancy R. and Thode,Jr, Henry C., Probabilistic measures of adequacy of a numerical searchfor a global maximum. J. Am. Stat. Assoc., 1989, 84(408), 1020–1023.
Holgate, P, Estimation for the Bivariate Poisson Distribution. Biomettrika, 1964, 51(1/2), 241-245.
Inbal, Y., & Galit, S, On generating multivariate Poisson data in management science applications. Applied Stochastic Models in Business and Ind¬ustry, 2012, 28, 91-102.
Kang, Qiang and Liu, Qiao, Stock trading, information production, and executive incentives. J. Corporate Finance, 2008, 14(4), 484–498.
Karlis, Dimitris and Xekalaki, Evdokia, Choosing initial values for the EM algorithm for finite mixtures. Computat. Stat. Data Anal., 2003, 41(3-4), 577–590.
Lee, Charles M.C. and Ready, Mark J., Inferring trade direction from intraday data. J. Finance, 1991, 46(2), 733–746.
Li, Haitao, Wang, Junbo, Wu, Chunchi and He, Yan, Are liquidity and information risks priced in the treasury bond market?. J. Finance, 2009, 64(1), 467–503.
Lin, Hsiou-Wei William and Ke, Wen-Chyan, A computing bias in estimating the probability of informed trading. J. Financ. Markets, 2011, 14(4), 625–640.
Madsen, L, & Dalthorp, D, Simulating correlated count data. Environmental and Ecological Statistics, 2007, 14, 129-148.
Tom, B., & Dimitris, K, A multivariate Poisson mixture model for marketing applications. Statistica Neerlandica, 2004, 58(3), 322-348.		zh_TW
dc.identifier.doi (DOI) 10.6814/NCCU202101482en_US